Methods and systems for providing interactive support sessions

ABSTRACT

An image processing system including at least one processor configured to receive image data captured by a mobile device image sensor. The image data includes images of an inoperative appliance. The processor is further configured to perform image recognition on the image data to identify the inoperative appliance and determine a likely cause of inoperability; retrieve a plurality of sequential instructions for enabling a user to remedy the inoperability; cause the mobile device to sequentially display the plurality of sequential instructions; detect that the inoperative appliance is outside a field of view of the image sensor, based on the image data; suspend display of additional sequential instructions when the inoperative appliance is outside of the field of view; detect when the inoperative appliance returns to the field of view; and resume display of the additional sequential instructions after the inoperative appliance returns to the field of view.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 16/392,972, filed Apr. 24, 2019, which is a continuation ofU.S. patent application Ser. No. 16/196,818, filed Nov. 20, 2018, whichis a continuation of U.S. patent application Ser. No. 15/366,483, filedDec. 1, 2016 (now U.S. Pat. No. 10,182,153). This application alsoclaims priority to U.S. Provisional Patent Application No. 62/669,407,filed on May 10, 2018, all of which are incorporated herein byreference.

TECHNOLOGICAL FIELD

The present disclosure generally relates to the field of imageprocessing, and particularly relates to systems and methods for usingimage processing to assist a user with technical support.

BACKGROUND

Technical support systems utilized nowadays make it difficult fordigital service providers (DSPs), and especially for service/technicalsupport centers, to provide efficient (in terms of time and customersatisfaction) technical support services to their customers. Despite arecent push toward self-service schemes, customer have been slow toadopt self-service technologies. Today's customers support model andrelevant technologies are subject to the numerous challenges, includingincreasingly complex customer needs, communication gaps, diagnosischallenges, limited problem solving rates, and customer frustration.

The techniques disclosed in this disclosure aim to provide remoteefficient consumer support services and reduce the incidence oftechnician dispatch. These techniques are useful for shortening consumerwait time, improving installation and repair outcomes, and improvingcustomer satisfaction and independence.

SUMMARY

One aspect of the disclosure provides an image processing system forvisually augmenting a real-time video stream, the image processingsystem, comprising: at least one processor configured to: receive thereal-time video stream captured by an image sensor, the real-time videostream including images of at least one cable and an electronicappliance; analyze the real-time video stream to identify a plurality ofports in the electronic appliance; analyze the real-time video stream toidentify a specific cable for association with a specific port of theplurality of ports; cause a movable augmented indicator to display onthe real-time video stream, wherein the movable augmented indicator isconfigured to guide a user's connection of the specific cable to thespecific port; monitor in the real-time video stream changing locationsof the specific port as the image sensor moves relative to theelectronic appliance; and adjust positions of the movable augmentedindicator to account for the changing locations of the specific port inthe real-time video stream

Another aspect of the disclosure provides a non-transitory computerreadable medium including instructions for visually augmenting areal-time video stream, the instructions being executable by at leastone processor to cause the at least one processor to perform a method,the method comprising: receiving the real-time video stream captured byan image sensor, the real-time video stream including images of at leastone cable and an electronic appliance; analyzing the real-time videostream to identify a plurality of ports in the electronic appliance;analyzing the real-time video stream to identify a specific cable forassociation with a specific port of the plurality of ports; causing amovable augmented indicator to display on the real-time video stream,wherein the movable augmented indicator is configured to guide a user'sconnection of the specific cable to the specific port; monitoring in thereal-time video stream changing locations of the specific port as theimage sensor moves relative to the electronic appliance; and adjustingpositions of the movable augmented indicator to account for the changinglocations of the specific port in the real-time video stream.

Another aspect of the disclosure provides a method for visuallyaugmenting a real-time video stream captured by an image sensor, themethod comprising: receiving the real-time video stream captured by animage sensor, the real-time video stream including images of at leastone cable and an electronic appliance; analyzing the real-time videostream to identify a plurality of ports in the electronic appliance;analyzing the real-time video stream to identify a specific cable forassociation with a specific port of the plurality of ports; causing amovable augmented indicator to display the real-time video stream,wherein the movable augmented indicator is configured to guide a user'sconnection of the specific cable to the specific port; monitoring in thereal-time video stream changing locations of the specific port as theimage sensor moves relative to the electronic appliance; and adjustingpositions of the movable augmented indicator to account for the changinglocations of the specific port in the real-time video stream.

Another aspect of the disclosure provides an image processing systememploying artificial intelligence during technical support, the imageprocessing system comprising: at least one processor configured to:receive image data captured by an image sensor of a mobile device, theimage data including images of an inoperative appliance in anenvironment of a user; perform image recognition on the image data toidentify the inoperative appliance and a likely cause of inoperability;retrieve a plurality of sequential instructions to be provided forenabling a user to complete a plurality of sequential actions in orderto remedy the inoperability; cause the mobile device to sequentiallydisplay the plurality of sequential instructions; detect that theinoperative appliance is outside a field of view of the image sensor,based on the image data and during execution of the sequential actions;suspend display of additional sequential instructions when theinoperative appliance is outside of the field of view; detect when theinoperative appliance returns to the field of view after suspendingdisplay; and resume display of sequential instructions after theinoperative appliance is detected to return to the field of view.

Another aspect of the disclosure provides a non-transitory computerreadable medium including instructions for employing artificialintelligence during technical support, the instructions being executableby at least one processor to cause the at least one processor to performa method, the method comprising: receiving image data captured by animage sensor of a mobile device, the image data including images of aninoperative appliance in an environment of a user; performing imagerecognition on the image data to identify the inoperative appliance anda likely cause of inoperability; retrieving a plurality of sequentialinstructions to be provided for enabling a user to complete a pluralityof sequential actions in order to remedy the inoperability; causing themobile device to sequentially display the plurality of sequentialinstructions; detecting that the inoperative appliance is outside afield of view of the image sensor based on the image data and duringexecution of the sequential actions; suspending display of additionalsequential instructions while the inoperative appliance is outside ofthe field of view; detecting when the inoperative appliance returns tothe field of view after suspended display; and resuming display of theadditional sequential instructions after detecting that the inoperativeappliance has returned to the field of view.

Another aspect of the disclosure provides a method for employingartificial intelligence during technical support, the method comprising:receiving image data captured by an image sensor of a mobile device, theimage data including images of an inoperative appliance in anenvironment of a user; performing image recognition on the image data toidentify the inoperative appliance and a likely cause of inoperability;retrieving a plurality of sequential instructions to be provided forenabling a user to complete a plurality of sequential actions in orderto remedy the inoperability; causing the mobile device to sequentiallydisplay the plurality of sequential instructions; detecting that theinoperative appliance is outside a field of view of the image sensorbased on the image data and during execution of the sequential actions;suspending display of additional sequential instructions while theinoperative appliance is outside of the field of view; detecting whenthe inoperative appliance returns to the field of view after suspendeddisplay; and resuming display of the additional sequential instructionsafter detecting that the inoperative appliance has returned to the fieldof view.

Another aspect of the disclosure provides an image processing systememploying artificial intelligence to electronically guide a technicalsupport session, the image processing system comprising: at least oneprocessor configured to: receive real-time image data captured by animage sensor of a mobile device at a location of an inoperative product,the real-time image data including at least one image of the inoperativeproduct; perform image recognition on the real-time image data toidentify a likely source of inoperability of the product; cause themobile device to display a plurality of sequential instructions formitigating inoperability of the product; determine that an error wasmade while performing the particular instruction, based on additionalreal-time image data captured following the display of a particular oneof the plurality of sequential instructions; cause the mobile device todisplay an error notification when the particular instruction is notcomplied with, wherein the error notification being displayed before asubsequent instruction is displayed; determine that the particularinstruction was subsequently complied with based on real-time image datacaptured following the notification; and cause the mobile device todisplay the subsequent instruction of the plurality of sequentialinstructions after the particular instruction is determined to have beencomplied with.

Another aspect of the disclosure provides a non-transitory computerreadable medium including instructions for electronically guiding atechnical support session, the instructions being executable by at leastone processor to cause the at least one processor to perform a method,the method comprising: receiving real-time image data captured by animage sensor of a mobile device at a location of an inoperative product,the real-time image data including at least one image of the inoperativeproduct; performing image recognition on the real-time image data toidentify a likely source of inoperability of the product; causing themobile device to display a plurality of sequential instructions formitigating the inoperability; determining that an error was made inperforming the particular instruction based on additional real-timeimage data acquired after the particular one of the plurality ofsequential instructions is displayed; causing the mobile device todisplay an error notification when the particular instruction is notcomplied with, wherein the error notification being displayed before asubsequent instruction is displayed; determining that the particularinstruction was subsequently complied with based on real-time image datacaptured following the notification; and causing the mobile device todisplay the subsequent instruction of the plurality of sequentialinstructions after determining that the particular instruction wascomplied with.

Yet another aspect of the disclosure provides a method forelectronically guiding a technical support session, the methodcomprising: receiving real-time image data captured by an image sensorof a mobile device at a location of an inoperative product, thereal-time image data including at least one image of the inoperativeproduct; performing image recognition on the real-time image data toidentify a likely source of inoperability of the product; causing themobile device to display a plurality of sequential instructions formitigating inoperability of the product; determining that an error wasmade in performing the particular instruction based on additionalreal-time image data captured following the display of a particular oneof the plurality of sequential instructions; causing the mobile deviceto display an error notification when the particular instruction is notcomplied with, wherein the error notification being displayed before asubsequent instruction is displayed; determining that the particularinstruction was subsequently complied with based on real-time image datacaptured following the notification; and causing the mobile device todisplay the subsequent instruction of the plurality of sequentialinstructions after determining that the particular instruction wassubsequently complied with.

Yet another aspect of the disclosure provides a non-transitory computerreadable medium for assisting a user during technical support, thecomputer readable medium including instructions that, when executed byat least one processor, cause the at least one processor to perform amethod, the method comprising: receiving at least one first image of aninoperative product captured by a mobile device of a user; performingimage analysis on the at least one first image to identify in the atleast one first image a status of a functional element associated withthe inoperative product; accessing memory to determine a reason why theproduct is inoperative based on the determined status of the functionalelement; causing visual guidance to be displayed by the mobile device,wherein the visual guidance is associated with a plurality of sequentialactions for causing the inoperative product to become operative;receiving at least one second image of the product, the second imagebeing indicative of a completion of the plurality of sequential actions;performing image analysis on the at least one second image to determinethat the completion of the plurality of sequential actions caused theinoperative product to become operative; and providing a notification tothe user indicating problem resolution.

Yet another aspect of the disclosure provides an image processing systememploying artificial intelligence for assisting a user during technicalsupport, the image processing system, comprising: at least one processorconfigured to: receive at least one first image of an inoperativeproduct captured by a mobile device of a user; perform image analysis onthe at least one first image to identify in the at least one first imagea status of a functional element associated with the inoperativeproduct; access memory to determine a reason why the product isinoperative based on the determined status of the functional element;cause visual guidance to be displayed by the mobile device, wherein thevisual guidance is associated with a plurality of sequential actions forcausing the inoperative product to become operative; receive at leastone second image of the product, the second image being indicative of acompletion of the plurality of sequential actions; perform imageanalysis on the at least one second image to determine that thecompletion of the plurality of sequential actions caused the inoperativeproduct to become operative; and provide a notification to the userindicating problem resolution.

Yet another aspect of the disclosure provides a method for assisting auser during technical support, the method comprising: receiving at leastone first image of an inoperative product captured by a mobile device ofa user; performing image analysis on the at least one first image toidentify in the at least one first image a status of a functionalelement associated with the inoperative product; accessing memory todetermine a reason why the product is inoperative based on thedetermined status of the functional element; causing visual guidance tobe displayed by the mobile device, wherein the visual guidance isassociated with at least one action for causing the inoperative productto become operative; receiving at least one second image of the product,the second image being indicative of a completion of the at least oneaction; performing image analysis on the at least one second image todetermine whether the at least one action was completed properly; andnotifying the user when the at least one action is completed properly.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIGS. 1A to 1C schematically illustrate support sessions according tosome possible embodiments, wherein FIG. 1A is a sequence diagram showingpossible stages in the communication establishment and FIGS. 1B and 1Cschematically illustrate exchange of video streams between a remote userand a support center;

FIG. 2 is a functional flowchart schematically illustrating a supportsession according to some possible embodiments;

FIG. 3 is a functional block diagram schematically illustratingcomponents of a control unit of a support center;

FIG. 4 is a flowchart illustrating an automated problem/defect detectionprocess employing deep learning, in accordance with some embodiments;

FIG. 5A is a block diagram illustrating a technical support system inaccordance with some embodiments;

FIG. 5B illustrates a decision tree used by the support system of FIG.5A in accordance with some embodiments;

FIG. 5C is a schematic illustration of a database record in accordancewith some embodiments;

FIGS. 6A-6C schematically illustrate an application of an imageprocessing system in accordance with some embodiments of the disclosure;

FIG. 7 is a flow chart illustrating a method for visually augmenting areal-time video stream captured by an image sensor;

FIGS. 8A-D illustrate an application of the image processing system inaccordance with embodiments of the disclosure;

FIG. 9 is a flowchart illustrating a method for employing artificialintelligence during technical support;

FIGS. 10A-10D illustrate an application of the image processing systemin accordance with embodiments of the disclosure;

FIGS. 11A-11F illustrate an application of the image processing systemin accordance with embodiments of the disclosure;

FIG. 12. is a flow chart illustrating a method for guiding a technicalsupport session;

FIGS. 13A-13D illustrate an application of the image processing systemin accordance with embodiments of the disclosure; and

FIG. 14 is a flow chart illustrating a method for assisting a userduring technical support.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions, or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

The present disclosure provides remote assistance techniques forefficiently identifying technical problems, defects and improperequipment configurations, and determining a most likely solution toresolve them. In one non-limiting example, the techniques disclosedherein can be used in interactive applications to assist with theinstallation and troubleshooting of various items, including, furniture,consumer electronics, and appliances. Such technologies may utilizelive, person-to-person video aided support and minimize consumerdissatisfaction and frustration.

In some embodiments, a processor of the system may receive image datacaptured by an image sensor of a mobile device. The term “mobile device”as used herein refers to any device configured to communicate with awireless network, including, but not limited to a smartphone, tablet,mobile station, user equipment (UE), personal digital assistant, laptop,wearable sensor, e-Readers, dedicated terminals, smart glasses and anyother movable device that enables communication with a remote server.

In some embodiments, the image data is captured by an image sensor. Theterm “image sensor” refers to a device capable of detecting andconverting optical signals in the near-infrared, infrared, visible, andultraviolet spectrums into electrical signals. The electrical signalsmay be used to form image data (e.g., an image or a video stream) basedon the detected signal. Examples of image sensors may includesemiconductor charge-coupled devices (CCD), active pixel sensors incomplementary metal-oxide-semiconductor (CMOS), or N-typemetal-oxide-semiconductors (NMOS, Live MOS). In some cases, the imagesensor may be part of a camera included in the mobile device. The term“image data” refers to any form of data generated based on opticalsignals in the near-infrared, infrared, visible, and ultravioletspectrums (or any other suitable radiation frequency range). Consistentwith the present disclosure, the image data may include pixel datastreams, digital images, digital video streams, data derived fromcaptured images, and data that may be used to construct a 3D image. Theimage data acquired by the image sensor may be transmitted by wired orwireless transmission to a remote server.

In some embodiments, the received image data may be a “real-time” videostream. The term “real-time” pertains to on-going image data whichclosely approximates events as they are occurring. Such real-time videostreams or “live” feeds allow, for example, a user to record an ongoingissue, installation, or repair at one location, and a remote customerservice agent to visualize the ongoing issue, installation or repair.

In some embodiments the image data may be annotated. The term“annotation” pertain to symbols, text, images or other visual aids forguiding the user during a technical support session.

In some embodiments, the received image data may include images of anelectronic appliance. The electronic appliance may include one or morefunctional elements. The term “functional element” means any componentof an electrical appliance that aids in the function or operation of theappliance. Such elements may include, for example, jacks, ports, cables,buttons, triggers, indicator lights and switches.

In some embodiments, the electronic appliance may be inoperative. Theterm “inoperative appliance” pertains to electrical appliances that arenon-operational because of non-assembly, defective assembly, defectiveinstallation or other defect. Such an appliance may be inoperativebecause it has not been plugged into a working electric source, becauseit has not been switched on or because a cable is in the wrong port(e.g., input instead of output).

In some embodiments, a processor of the system may cause the mobiledevice to display visual guidance for mitigating inoperability of aproduct (e.g. electrical appliance, furniture etc.). The term “visualguidance” pertains to any visual aid for directing a user to aparticular component or group of components of the product (e.g., legsof a table or port of a cable box).

There is an ongoing demand for efficient customer service centerscapable of quickly diagnosing and efficiently resolving problemsencountered by their remote users. Traditional voice call supportparadigms are rarely capable of establishing efficient and costeffective customer support centers. These conventional telephone voicecall based support centers identify a limited number of user issuesthrough tedious interrogation of their remote end users. In manyinstances an information gap exists between the center and the user. Theend user may be incapable of correctly defining theirproblems/difficulties and thus unable to provide the support center withmeaningful information for solving it. As a result, the center mustdispatch a skilled technician to resolve the problem at the user'sremote site (e.g., home, office, etc.).

The present application provides systems and methods for closing theinformation gap by enabling support centers to quickly identify productdefects and other issues at the remote user's site, and for renderingfast and accurate working solutions. The amount of information exchangedbetween the remote end user and the support center is facilitatedthrough use of a mobile device that allows for the exchange of imagedata and sound.

Upon establishing a video support session, the support center processesand analyzes the sounds and image data received from the remote enduser. The support center provides tools for conveying instructions tothe remote user. For example, where image data is comprised of one ormore still images, the support center may add annotations, e.g., text,signs and/or symbols to the image data. Where image data is comprised ofreal-time video stream or video frames, the support center maysuperimpose a movable augmented indicator onto the image data. Theannotated or superimposed image data is presented on the display of theuser's mobile device. When the remote user successfully resolves theproblem by following the annotated/superimposed instructions, theproblem and solution is stored in a cloud or other database recordsystem. By storing various problems and solutions, a database of workingsolutions is gradually established. The database may be used by thesupport center to more quickly and efficiently solve future problems.Alternatively, the database may form an artificial intelligence, wherebythe artificial intelligence and not an agent of the support centersolves the problem using the image data and relays the instructions tothe user using annotations or a moveable augmented indicator asdescribed above.

The disclosed methods and systems utilize computer vision tools havingtracking capabilities for identifying complex objects/elements within amobile device field of view, and for allowing tracking of such complexobjects in sophisticated/challenging visioning conditions (e.g., poorlighting, poor resolution and noisy conditions). In some embodimentsthese capabilities are implemented by use of neural network tools. Thisway a multitude (e.g., thousands) of video streams can be analyzed bysystems implementing the techniques disclosed herein to assist in thetechnical support sessions thereby conducted.

The present application thus provides systems and methods for conductingremote technical support sessions and real-time instructions. Themethods and systems disclosed herein allow a technical support agent tosee, in real-time, image data captured by the user's mobile device, andto provide real-time solutions.

Various tools continuously maintain the database records and discarddatabase records that are not relevant or valid, inefficient, and/orrendered obsolete, and to facilitate real time matching of best workingsolutions from the database records to ongoing support sessionsconducted by the support center. The techniques disclosed herein can bethus used to develop artificial intelligence (e.g., visual cellularchatbots) configured to provide automated customer support services.

Optionally, the video support session between the support center and theuser's device may be activated by the user after receiving an activationlink embedded in a text message (e.g., SMS, WhatsApp, email etc.) sentfrom the support center. By clicking on/accessing the embedded link theuser opens the support session described herein and establishes thevideo support session communication with the support center. Optionally,the support session is achieved by means of an application installed onthe user's device and configured to establish the video support sessioncommunication with the support center.

Support sessions techniques and systems are disclosed, wherein abidirectional video communication is established between a technicalsupport person and a remote user to provide additional layers ofinformation exchange therebetween, thereby expanding the abilities ofthe system to detect issues/defects in appliances, objects andequipment, and of illustratively conveying solutions to the remoteend-user. The bidirectional video communication can be achieved withoutrequiring installation of a dedicated video support session applicationin the user's device. In some embodiments, after a telephone call isreceived from the remote user, the expert/agent verifies that theend-user owns a smartphone, and then sends a link (e.g., embedded in atext message) to the user's device. The bidirectional video stream isestablished once the customer clicks on the link received from thesupport center. The bidirectional video communication enables theexpert/agent to see the environment at the remote site of the user, ascaptured by the back camera of the user's device, and thereby allowsproviding the remote user with substantially accurate instructions forresolving the encountered problem.

FIGS. 1A-1C schematically illustrate support sessions in accordance withcertain embodiments. Turning to the figures, FIG. 1A depicts a diagramillustrating various steps of a support session 20 and FIGS. 1B and 1Cillustrate communications between a remote user 33 and an agent 36 p ofa technical support center (TSC) 36 during a video support session 20.Support session 20 is initiated when a user 33 calls or otherwisecontacts TSC 36 using a mobile device 31. Initiation (step A1) may beperformed over a cellular and/or landline network, or othercommunication channels e.g., satellite communication, voice over IP,etc. When a call is received, TSC 36 sends a message (step A2) to themobile device 31. The message may be SMS, email, WhatsApp, etc. andcomprises a link (e.g. URL) for commencing a the support session. Uponopening the link (step A3), mobile device 31 accesses a remote server 36s over a data network 32, wherefrom video support session 21 setupinstructions/code are sent to the mobile device 31 to establish thesession (step A4). The remote server 36 s may be implemented as part ofthe support center and/or in a cloud computing infrastructure accessiblefor both the users and the support center.

Once the code is entered/instructions followed, the support sessionbegins. Agent 36 p may request the user's permission to access andactivate an image sensor, e.g. camera, on the mobile device 31 (stepA5). Once user 33 approves the request (step A6), the camera isactivated (step A7). User 33 may describe the problem and/or reasons forwhich the support is needed after which agent 36 p may direct user 33 todirect the camera on the impaired equipment, appliance or other objectof interest 33 e. The session enables agent 36 p to simultaneously viewimage data 33 i received by mobile device 31 and speak with the user(audio-visual support) (step A8). Image data 33 i includes the impairedequipment, appliance, or other object of interest and is displayed on adisplay device 36 d at TSC 36 for agent 36 p to see. Image data 33 i mayinclude real time video, still images, etc. and may or may not includeaudio support depending on agent/user preference or network bandwidth.Support session 20 may be conducted iteratively in real time until theuser's problem is resolved. If the agent is unable to resolve the user'sproblem, a skilled technician might be sent to resolve the user'sproblem.

The techniques described herein enable agent 36 p to superimposeannotations and/or moveable augmented indicators (markers) on the imagedata in real time. The markers and/or annotations may be createdmanually or selected from a pre-prepared library. Thesemarkers/annotations are superimposed on the object of interest and/or afunctional element thereof within the image data during the videosession. This may be achieved using a video tracker 36 t so that themarkers/annotations remain anchored to the object even if image sensor31 c moves relative to object 33 e. Video tracker 36 t may beimplemented in a control unit 12, or configured to operate in a remoteserver/the cloud. Tracker 36 t is configured and operable to track oneor more objects 33 e within image data 33 i received from the remoteuser 33 and re-acquire the tracked object if it disappears from theimage sensor's field of view and reappears due to movements of the imagesensor 31 c.

Video tracker 36 t is configured to track the relevant objects/elementswithin the image data and to anchor annotations/markers when they movewithin the image data. Anchoring the annotations/marker to the object orparticular element of the object allows the user to better track theobject/element of interest, thereby facilitating assembly or repairthereof. In some embodiments, the object is annotated/marked using acomputer vision tool. When the image data is received, the algorithm mayidentify its functional elements and brand. Once the identification isverified by the agent or through speech analysis, the data is markedaccordingly.

Alternatively, snapshots of image data 33 i may be taken and markers orannotations may be superimposed on the snapshots, then sent to the user.Image data 33 i and data related to the support session, e.g., repairtime, assembly time, solutions, annotations, markers, etc. may be savedto one or more databases 36 r. Optionally, image data may be filtered sothat background images, e.g., the room in which the object is located,are removed.

As show in FIG. 1B, TSC 36 may include control unit 12. Control unit 12is configured and operable to display the image data received frommobile device 31 on display device 36 d and provide agent 36 p withimage processing tools for adding the annotations or markers onto theimage date received from mobile device 31. Control unit 12 is furtherconfigured and operable to send the annotated/augmented image data 33 i′to the mobile device 31. In this way, the user is able to view theannotations/markers on the mobile device.

FIG. 1B further illustrates server 36 s. Server 36 s is used to conductthe video support session between user 33 and TSC 36, and may beimplemented in the TSC 36, and/or in remote data network 32, e.g., in aserver farm or in a cloud computing environment. Optionally, the remoteserver may also be configured to carry out some of the tasks of thecontrol unit 12, such as but not limited to, AR functionality, trackerfunctionality, image recognition and/or processing.

As illustrated in FIG. 1C, TSC 36 may provide agent 36 p with augmentedreality tools for superimposing real-time annotations and/or markers 39onto the image data acquired by the mobile device image sensor 31 c. Theannotations and/or markers can be manually added to the image data usinga pointing/mouse device and draw tool/module, and/or any imageprocessing utility having freehand abilities. The annotated image data33 i′ is then transferred over the data network 32 to the user's mobile31 and displayed before the user.

Additionally, or alternatively, the agent 36 p may select pre-definedannotations and/or markings and place them in the acquired image data 33i. In some embodiments an embedded video tracker is used to superimposethe annotations and/or markers 39 onto a desired object 33 e within theimage data. The tracker may move and/or resize the annotations and/ormarkers 39 whenever the image sensor 31 c moves relative to object 33 e.In this way, the position and/or size and of the annotations/markers maychange in response to relative movement between image sensor 31 c andobject 33 e. In some embodiments, AR tools may be used to create anassociative connection between two or more objects in image data 33 i.For example, an arrow may be superimposed between a particular cable anda particular functional element, e.g., port, of the object, to informthe user that the designated cable needs to be inserted into thedesignated port.

Using these techniques, a bidirectional video communication isestablished whereby the agent 36 p is provided with image data 33 idepicting the setup/configuration of the impaired appliance, equipmentor object 33 e, and the user 33 is provided with instructive annotatedand/or augmented image data 33 i′.

In some embodiments, TSC 36 is configured to record the video supportsessions 21 in a database 36 r. This way, the TSC 36 builds acontinuously growing audio/visual database of user problems, and oftheir corresponding working solutions. The database may be used bycomputer vision tools to facilitate resolving of user's problems infuture technical support sessions. Optionally, database 36 r may bestored in a network computer/server of the TSC 36 (e.g., in the cloud).

FIG. 2 is a functional flowchart illustrating support session 20 inaccordance with some embodiments. The support session 20 commences whenuser 33 contacts TSC 36 to describe a particular issue (step S1). Wherethe user 33 calls the TSC 36 and verbally describes the issue (step S2),auditory signals received from user 33 are processed and analyzed byspeech analysis tools to extract keywords therefrom (step S3). Thesekeywords are associated with the issues at hand and are indicative ofthe type of equipment, appliance, object, etc. 33 i needing support.faulty item/equipment and/or the nature of the encountered problem.

After TSC 36 receives enough information from user 33, an audio/visualsupport session is established (step S4). Image data 33 i received fromuser mobile device 31 is processed by the TSC 36 using a deep learningalgorithm to detect the problematic appliance, equipment or object 33 ein the image data (step S5), and to identify possible issues/defectscausing the problem encountered by the remote user 33 (step S6).

Optionally, TSC 36 may utilize an embedded on-line computer vision toolconfigured and operable to automatically identify the relevant objectsin the image data 33 i, and/or identify codes, text and other symbolsonto object 33 e to thereby enable TSC 36 to identify the type, make,serial number etc. of the faulty object 33 e. In some embodiments, agent36 p may guide the computer vision tools as to what objects to look forin image data 33 i. For example, agent 36 p may guide a cursor of apointing device (e.g. a mouse) on/near the objects/elements thereof.

Additionally, or alternatively, speech analysis tools may be used toanalyze the user's speech to identify keywords within the speech and aidthe computer vision tool as it processes image data 33 i for relevantobjects/elements within the image sensor field of view. For example, ifthe speech recognition tool identifies words such as internet/networkand communication/connectivity in the user' speech, it may guide thecomputer vision tool to look for LAN sockets or cables, Wi-Fi antennasand/or LEDs indications. Optionally, the keywords may be typed by agent36 p. Upon identifying the relevant objects in the image data 33 i, theTSC system using the computer vision tool can analyze the object'ssetup/configuration and automatically identify potential issues/defectstherein. Display device 36 d may be used to present to the identifiedobject to the agent.

Once the issue is identified, agent 36 p may instruct the user on how tosolve it. If the solution is relatively simple, (e.g., press the powerswitch), agent 36 p may provide verbal instructions. If user 33 isunable to carry out the verbal instructions, or the instructions arerelatively complex, agent 36 p may generate an instructive augmentedreality video stream using one or more markers 39 and trackers (stepS8). The markers are superimposed onto the image data and displayed onthe user's mobile device in real time to provide additional guidance.The agent may alternatively superimpose annotations as discussed anddescribed above with respect to FIGS. 1A-1C.

Optionally, TSC 36 may query database 36 r for a best working solution(step S7), based on the object's determined issues/defects, andtransmits the best working solution to user 33. The instructions maycomprise textual, auditory and/or annotated/augmented content. Agent 36p may provide user 33 with some (including one) or all types ofinstructive content, and/or limit the content to include some or theentire set of instructions.

After presenting proposed solution(s) to user 33 via mobile device 31(step S9), the user performs the instructions received from the TSC 36.During this stage, image data 33 i is continuously streamed from user'sdevice 31, thereby allowing agent 36 p to supervise and verify whetheruser 33 is following the instructions correctly, and provide correctiveguidance if user 33 performs an action incorrectly. If the instructionsprovided do not resolve the problem, agent 36 p may attempt to detectalternative issues/defects. If user 33 is able to resolve the problemusing the provided solution, data relating to the support session 20 isrecorded in the database 36 r at the TSC 36 (step S11). The new record51 may include data related to the resolved problem, e.g. keywords usedby the system to identify the issues/defects, the objects/elements inwhich the issues/defects were found, and image data 33 i received by theTSC 36 and/or annotated/augmented image data 33 i′ conveyed to theremote user for resolving the problem.

The TSC is configured to use image data 33 i to learn the nature of theissue/defect encountered, the best working solutions based on previousrelated sessions, and construct database records cataloguing thesuccessful solutions. Database 36 r is configured to continuouslyproduce increasingly efficient solutions and improve customersatisfaction.

Periodic/intermittent maintenance procedures may be used to guaranteethe effectiveness and validity of the records stored in the database. Insome embodiments each database record is monitored during themaintenance procedures to determine its successful problem resolvingpercentage (rank) in real-time technical support sessions 20. Forexample, each solution may be ranked/scored according to the totalnumber of times it was successfully used to resolve a specific problemor the total number of times it failed to resolve the problem. In someembodiments, maintaining the database further comprises discardinglow-ranking solutions and only maintaining high-ranking solutions. Suchdatabase maintenance procedures increase the chances of successfullyresolving user problems in future technical support sessions by usingthe good working examples used in the past to resolve the same problems.

Big data mining algorithms (dedicated for images and video i.e., videoanalytics tools) can be used to continuously monitor the database tosort and classify the problems and solutions that form the base line forthe deep learning algorithms. Initially, mining may be done manually,whereby the agent scans the most relevant support sessions andclassifies them according to problems they dealt with. Next, using themining algorithm, the TSC may scan the support sessions and classifythem automatically based on the keywords, objects/elements identified inthe session. In some embodiments snapshots of the image data areautomatically taken and the objects/elements therein are classified andadded to the database for the computer vision tool discussed above.Optionally, image data 33 i may be filtered so that background images,e.g., the room in which the object is located, are removed.

Deep learning algorithms can be used to analyze classified image data,and deliver the best working solution based on the “lessons” learnedfrom all the past support sessions related to a certain class ofproblem.

FIG. 3 is a functional block diagram illustrating components of thecontrol unit 12 depicted in FIG. 1B. A processing utility 12 p is usedto process image data 33 i received from user mobile device 31, identifyissues/defects related to the captured object 33 e, and generateannotated/augmented image data 33 i′. Processing utility 12 p comprisesa speech analysis module 12 s configured and operable to process theauditory signals received from the user and identify keywords indicativeof the problematic/defective object, its elements and/or the nature ofthe problems experienced by the user. Processing utility 12 p furthercomprises an image recognition module 12 i configured and operable toprocess image data 33 i from the user's mobile device and detectobjects/elements related to the problem to be resolved.

Image recognition module 12 i may scan the image data for certainobjects/functional elements using keywords identified by the speechanalysis module, and/or keywords 36 i selected by the agent. In someembodiments, an optical character recognition (OCR) module 12 c is usedto identify letters/symbols within image data 33 i, which can be used toguide the speech analysis module 12 s and/or the image recognitionmodule 12 i.

Control unit 12 is configured and operable to use image recognitionmodule 12 i to identify an object/functional element's setup/configuration and detect potential problems or defects therein.Database 36 r can be used to store a plurality of erroneoussetups/configurations (also referred to herein as reference data) to becompared by a comparison module 12 u of control unit 12. Whenever thecomparison module 12 u determines that newly acquired image data 33 icontains the same objects, issues or defects as the reference data,control unit 12 generates a diagnosis 12 d identifying the erroneoussetup/configuration identified in the image data 33 i.

Processing utility 12 p may further include an image processing module12 g. Image processing module 12 g is configured to superimposeannotations or markers onto the image data 33 i based on keywords 36 i,and generate annotated/augmented image data 33 i′ for conveying to theremote user. Control unit video tracking module (tracker) 36 t ensuresthat the annotations/markers superimposed onto the image data remainanchored to a desired object or functional element while the imagesensor and/or object move.

Whenever a support session conducted by TSC 36 successfully resolves aproblem encountered by a remote user 33, control unit 12 generates a newdatabase record 51 including data related to the user's problem and theTSC's solution. The new database record is stored in the database 36 rfor use in future support sessions conducted by the TSC. In someembodiments, one or more databases 36 r are used to store the referencedata and the records 51.

FIG. 4 is a flowchart illustrating an automated problem/defect detectionprocess 40 employing deep learning, in accordance with some embodiments.Image data 33 i received from image sensor 31 c of the user's mobiledevice 31 is processed by the image recognition module 12 i to identifyobjects/elements related to the user's problem. As discussed above withrespect to FIG. 3, image recognition module 12 i may receive keywords 36i from the agent 36 p and/or the control unit 12 to look for objectsrelated to the user's problem. Alternatively, the speech analysis module12 s may be used to process the auditory signals obtained from the user33 to identify keywords uttered by the user, and/or the agent, duringthe session 20. Keywords deduced from the auditory signals may then beused to guide the image recognition module 12 i in identifying in theimage data 33 i objects/elements related to the problem to be solved.Additionally, or alternatively, the keywords may be typed by the agentduring the support session.

Once object(s) 33 e are identified as relevant, the image data undergoesdeep image recognition 42 to identify the object's setup/configuration43. A comparison step 45 is then used to compare the identifiedsetup/configuration 43 to setup/configuration records 44 stored indatabase 36 r of TSC 36. Based on the comparison results, the processproceeds to step 46, wherein a determination is made as to whether theassessed and recorded setups/configurations match.

If the setups/configurations do not match, the auditory and/or imagedata are reprocessed, to identify new objects/elements 33 e. Any newauditory and/or image data obtained during the session 20 may also beprocessed, at which point all auditory/image data are processed toidentify potential setup/configuration problems/defects. If a match isdetected at step 46, possible problems/defects are determinedaccordingly in step 47. Comparing new problems against a database ofpotentially related problems allows for precise problem identification.Once problems/defects are determined, database 36 r is queried in step48 for the best past solution as discussed above with respect to FIG. 2.After determining the best past solution, it is presented to the uservia a display in the mobile device in step 49. The best past solutionmay include an annotated image, a video showing how to achieve the bestproblem solution (with or without AR markings), text and/or audibleinstructions, and is presented to the user via the display of the mobiledevice 31.

Although generally configured to match an identified setup/configuration43 with known, defective/problematic configurations in the database, thecomparison conducted in step 45 may also be configured to match theidentified setup/configuration 43 with non-problematic/non-defectivesetup/configurations. If the identified configuration 43 is determinedto be correct, the image data and/or auditory signals undergoes furtherprocessing using speech analysis module 12 s and/or image recognitionmodule 12 i. If there is no match between the setups/configurations, theitems/elements causing the mismatch are analyzed to determine potentialproblems/defects in step 47.

If it is determined in step 50 that the best past solution obtained insteps 48-49 resolved the user's problem, a new database record 51 isconstructed in step 54, and then stored in the database of the systemfor use in future trouble shooting sessions. The new database record mayinclude one or more annotated images, a video showing how to fix theproblem (with or without AR markers), text and/or audible instructions.If the best past solution is unsuccessful, other high-ranking solutionsare obtained from the database, and presented in attempt to resolve theproblem. Steps 48 to 50 may be repeated for each solution until asuccessful solution is found. Alternatively, or concurrently, speechanalysis 12 s, image recognition 12 i, and steps 41 to 46 can be carriedout to determine alternative problems/defects in the object 33 e. If theproblem is not resolved using a predefined number of database solutions,agent 36 p may provide supplemental instructions or send a professionalexpert to the user 33 in step 52.

Through repetition, the problem/defect detection process 40, image dataanalytics, and algorithms disclosed herein establish a self-servicemechanism in which the computer vision tools are used to analyzeobjects/elements in image data 33 i and identify problematic/defectivesetups/configurations. This automation allows TSC 36 to diagnose objects33 e in real-time and produce expedient solutions. In one non-limitingexample, techniques and processing system disclosed herein are capableof identifying disconnected cable(s), cable(s) that are erroneouslyconnected to the wrong port/sockets, errors indicated by certain LEDs,and/or error messages.

In some embodiments, once the problem/defect detection process 40identifies an object's problems/defects, tracker 36 t automaticallytracks the relevant object as discussed above with respect to FIG. 1B.Tracking facilitates problem/defect identification by allowing the agentto anchor annotations/markers to the object. This is especially usefulwhere the user is technically unskilled. This automated problems/defectsidentification may be provided in instead of, or in addition to agentsupport.

The database generation and sorting process disclosed herein may be usedin each support session 20 to improve the system's performance andfacilitate customer service. Optionally, a machine learning process(e.g., employing any suitable state of the art machine learningalgorithm having machine vision and deep learning capabilities) may beused to troubleshoot the technical support sessions. In one non-limitingexample, the machine learning process logs and analyzes the users'interactions with the system during the support sessions to identifycommon users' errors. In this way, a dynamic database is constructed andproblem solving sessions are optimized.

FIG. 5A is a block diagram illustrating a technical support system 50 inaccordance with some embodiments. Support system 50 maintains andutilizes database 36 r to resolve user problems. Support system 50 usesa machine deep learning tool 52 to process and analyze image andauditory data received from a plurality of support sessions 20 in realtime. Machine deep learning tool 52 classifies each of the live supportsessions 20 in a specific problem group (e.g., LAN connectivity,wireless connectivity, bandwidth, data communication rates, etc.), andidentifies keywords and/or objects/elements mentioned/acquired duringthe session.

In some embodiments, the machine deep learning tool 52 is used toperform high-resolution, in depth, image recognition processes toidentify the setup/configuration of object(s) 33 e, as they appear inthe image data. Identifying the setups/configurations allows the machinedeep learning tool to accurately classify each support session incorrect problem group. As will be described in detail below, the machinedeep learning tool 52 may be used to find best matching solutions 55. Inthis way, machine deep learning tool 52 can be used to provide thesystem 50 a layer of automation, thereby allowing it to solve the user'sproblem without agent intervention.

Optionally, machine deep learning tool 52 may be configured and operableto carry out computer vision and video analysis algorithms for analyzingthe image data and autonomously detecting problematic/defective objectstherein. Machine deep learning tool 52, and/or processing utility 12 pmay then use the detected problems/defects to determine which pastsolutions from the database 36 r may be used by agent 36 p to resolve anongoing user problem.

In some embodiments, machine deep learning tool 52 is further configuredand operable to process and analyze data records 51 relating topreviously conducted support, classify the database records according tothe type of problem dealt with in each database record 51, identifykeywords and/or objects/elements mentioned/acquired during the supportsession, and rank/weigh each database record 51 according to the numberof times it was successfully used to resolve a problem of a particulartype/classification.

FIG. 5B illustrates a decision tree used by support system 50 fordetermining the sequential instructions required to remedy theinoperability of an appliance. The input for the decision tree may bedata derived from image data captured by image sensor 31 c. For example,the image data may depict a current state of the inoperative product.The nodes in FIG. 5B represent operative states of the inoperativeproduct and the lines represent the actions or the steps the user needsto complete to render the inoperative product operative.

At node 57 a, support system 50 may identify the inoperative product(e.g., a router) using, for example, the router's model number. In step58 a the instruction may be to connect different cables of the router ina certain order. At node 57 b, support system 50 may use additionalimage data to determine whether indicator lights of the router are redor green. If the indicator light is green, support system 50 mayinstruct the user to preform action steps 58 b and 58 c. If theindicator light is red, or otherwise not green, the support system 50may instruct the user to perform other actions. Each line (i.e. action)may be associated with a weight representing the probability that theaction will resolve the product's inoperability. Support system 50 mayselect a next sequential action based on the associated weight as willbe discussed in further detail below. A person skilled in the art willrecognize that the above example is simplified and that the actualprocess of remedy the inoperability of a product may involvesubstantially more steps.

FIG. 5C schematically illustrates a database record 51 in accordancewith some embodiments. In some embodiments, the database record 51 mayinclude an identifier field 51 a which describes the object 33 e using,e.g., the object's serial or model number, a classification field 51 bdescribing the type of problem/defect associated with the object, aranking field 51 c comprising a list of solutions, a keywords/objectsfield 51 d cataloguing the keywords and objects mentioned/acquiredduring the support session, and a session data field 51 e, whichincludes image and auditory data related to the support session and usedto resolve the user's problem.

It is to be appreciated that the systems and methods disclosed hereinfacilitate problem identification and resolution by substantiallyautomating the process. Such automation reduces the agent training timeand expense.

In some embodiments, machine deep leaning tool 52 is configured to scandatabase records 51 during each support session to match the problempresented in an ongoing session with a best matching solution 55.Machine deep leaning tool 52 identifies a set of database records 51whose classification field 51 b matches the classification field 51 b ofan ongoing support session 20. The machine deep leaning tool 52 thencompares the keywords/objects field 51 d of each database records 51 inclassification field 51 b to the keywords/objects identified/acquired inthe ongoing support session 20, and selects a sub-set of best matchingsolutions 55. Thereafter, machine deep leaning tool 52 compares rankingfields 51 c of the sub-set of best matching solutions 55 and selects atleast one high-ranking solution 55 therefrom. The solution is then usedby system 50 to resolve the problem presented in the ongoing supportsession 20.

In some embodiments, system 50 comprises a maintenance tool 56configured and operable to operate in the background and continuously,periodically or intermittently, check the validity of each one ofrecords 51 of database 36 r. Maintenance tool 56 can determine whethercertain types of database records 51 are no longer relevant (e.g.,whether the record relates to obsolete/outdated technologies) and thuscan be discarded. Maintenance tool 56 may also discard database recordsthat are infrequently used, or which had little (or no) success inresolving user's problems.

In some embodiments maintenance tool 56 comprises a classificationmodule configured and operable to classify the database records, and/orverify the classification determined for each record by machine deeplearning tool 52. As illustrated in FIG. 5A, a weighing tool 56 w can beused to weigh and/or ranks each database record 51. The weight/rank maybe assigned to designate the relevance of a record 51 to a certainclassification group, such that each record may have a set of weightsindicating a measure of relevance of the record to each one of theproblems classifications/categories dealt with by the system. A rank maybe assigned to a record to designate a score/percentage indicative ofthe number of instances it was successfully used to resolve problems insupport sessions conducted by the system 50.

As illustrated in FIG. 5A, system 50 may further comprise a filteringmodule 56 f. Maintenance tool 56 may use filtering module 56 f todetermine whether to discard one or more database records 51. Thefiltering module 56 f is configured and operable to validate thedatabase records and decide accordingly which of the database records 51provide valuable solutions and should be maintained. Optionally, thefiltering module 56 f may be configured and operable to maintain onlydatabase records having a sufficiently high rank, e.g., above somepredefined threshold, and discard all other records 51. Alternatively,the filtering module 56 f is configured and operable to examine theranks of all database records 51 belonging to a certain classificationgroup, maintain some predefined number of database records having thehighest ranks within each classification group, and discard all otherrecords 51 belonging to the classification group. For example, five (ormore, or less) records having the highest ranks/scores may be keptwithin each classification group.

As explained hereinabove, the techniques disclosed herein allow for thegradual implementation of full self-service, wherein system 50autonomously analyzes auditory/image data from the user's mobile device31 and automatically determines the potential issues/defects causing theuser's problem(s). System 50 is capable of automatically and remotelyguiding the user to fix the problem, while monitoring the user's actionsin real time.

System 50 can be thus configured to concurrently conduct a plurality ofsupport sessions 20, without any human intervention, using combinedspeech and image/video recognition techniques, to extract the proper andrelevant keywords from auditory signals and/or image data obtained fromuser 33 that describe the experienced problem, and to determine thesetup/configuration of the user's object.

The combinations of the speech and image/video recognition techniquesdisclosed herein enable system 50 to assess the nature of the user'sproblem, and to generate a set of best working solutions from thesystem's database of relevant past working solutions. By using deepmachine learning tools, the system gains enhanced problem solvingcapabilities, thereby guaranteeing that common user problems will getthe best solutions.

In some embodiments, database records may be stored on user mobiledevice 31. This is especially useful when mobile device connectivity ispoor or non-existent, or connectivity is unnecessary. These databaserecords 51 are relevant to one or more of the user'sproblematic/defective objects/equipment. In such embodiments user mobiledevice 31 can be configured to automatically identify theobject/equipment that needs to be serviced, using any of the techniquesdescribed herein, or alternatively let the user select theitem/equipment that needs to be serviced from a list. Based on theuser's selection, and/or automatic identification, user 33 will beprovided with the best working solutions as provided in the maintaineddatabase records e.g., by playing/showing the recorded augmented realitybased instructions. This way, different and specific self-servicesupport modes can be implemented in a user's device, according to thespecific items/equipment of the user.

If the user's device has temporary connectivity over a communicationnetwork (e.g., to the cloud), the best working solution can bedownloaded to the user's device using the same user selection, and/orautomatic identification procedures, either manually or by patternrecognition techniques.

One inventive aspect of the disclosure provides an image processingsystem for visually augmenting a real-time video stream. The imageprocessing system comprises at least one processor configured to receivethe real-time video stream captured by an image sensor, the real-timevideo stream including images of at least one cable and an electronicappliance. The at least one processor may analyze the real-time videostream to identify a plurality of ports in the electronic appliance. Theat least one processor may also analyze the real-time video stream toidentify a specific cable for association with a specific port of theplurality of ports; causing a display of a movable augmented indicatortogether with the live video stream, wherein the movable augmentedindicator is configured to guide a user's connection of the specificcable to the specific port. The at least one processor may furthermonitor in the real-time video stream changing locations of the specificport as the image sensor moves relative to the electronic appliance. Insome cases, the at least one processor may adjust positions of themovable augmented indicator to account for the changing locations of thespecific port in the real-time video stream.

In accordance with some embodiments, the system is configured to displaya movable augmented indicator together with the real-time (live) videostream to account for varying distances between two objects.

FIGS. 6A-6C schematically illustrate an application of image processingsystem 60 for visually augmenting a real-time video stream. During afirst phase (T1) user 33 positions a mobile device 61 such that object63 e and functional elements 63 f, 63 g and 63 h are within the field ofview of the mobile device's image sensor. In some embodiments, object 63e may be an electrical appliance and functional elements 63 f-h may becables for connecting to the electrical appliance and ports forreceiving the cables.

Mobile device 61 may transmit, and image processing system 60 mayreceive, image data 33 i in the form of a real-time video stream. Oncereceived, system 60 may analyze the video stream to identify theelectrical appliance and functional elements thereof, using the database36 r and data processing methods discussed and described above withrespect to FIGS. 3 and 5. After each element is identified, a moveableaugmented indicator may be superimposed onto the video stream during asecond phase (T2). As discussed above, augmented indicator 64 mayprovide instructive guidance to user 33 to facilitate repair and/orassembly of the electrical appliance. The indicator may appear on mobiledevice 61 during the live video stream and may appear in any of avariety of shapes or symbols to facilitate repair/assembly. FIG. 6Aillustrates an arrow-shaped augmented indicator 64 a directing the userto insert cable 63 g into a specific port. FIG. 6b illustrates analternative embodiment in which a first, finger-shaped augmentedindicator 64 b directs the user's attention to cable 63 g and a second,circular augmented indicator 64 c circumscribes the specific port.Optionally, a confirmation notification may be superimposed on the livevideo feed once a repair/assembly step is successfully completed. FIG.6C illustrates a confirmation notification 65 in the shape of a check;it is to be understood however, that confirmation may be provided usingany of a variety of symbols, messages or sounds. For example,confirmation 65 may include the message “OK”, a green circle, an audiblechime etc. Alternatively, the moveable augmented indicator may change(e.g., color or disappear) to indicate completion.

In one embodiment, image processing system 60 may utilize the tracker 36t, discussed and described above, to adjust a position of indicator 64when the image sensor, appliance or functional elements move. Forexample, arrow 64 a may become shorter or shift position, or circle 64 cmay become smaller as the user inserts the cable into the port. Imagerecognition and processing modules 12 i, 12 g of the processor mayrecognize functional elements when they are being manipulated. In thisway, the processing system is able to identify and track the applianceand functional elements while they are being held by the user.

FIG. 7 is a flow chart illustrating a method for visually augmenting areal-time video stream captured by an image sensor using imageprocessing system 60. The method starts when image processing system 60receives real-time video stream 33 i captured by an image sensor on theuser's mobile device (step 70). The real-time video stream 33 i mayinclude images of at least one object, e.g., electronic appliance 63 eand one or more functional elements. In some cases, system 60 mayvisually (e.g. via textual command) or audibly (e.g. via voice command)direct the user to place the object and one or more functional elementswithin the sensor's field of view, or otherwise refocus the imagesensor.

Once within the field of view, the real time video stream 33 i may beanalyzed using the data processing techniques described above toidentify one or more functional elements, e.g., a plurality of ports inthe electronic appliance and a specific cable for association with aspecific port of the plurality of ports (step 72). The received videostream may be captured by a wearable image sensor or mobile device. Insome embodiments, the at least one processor may be configured toanalyze the video stream to identify a user's hand and one or morefingers, and/or an additional cable associated with an additional portof the plurality of ports when the specific cable is held by the user'shand or one or more fingers.

After identifying the relevant elements, the system 60 may cause amovable augmented indicator to display on the live video stream, themovable augmented indicator may be configured to guide the user'sconnection of the specific cable to the specific port (step 74). In someembodiments, the augmented indicator may include a boundary at leastpartially circumscribing the specific port. The boundary may form atleast a portion of a circle, square or other polygon. Alternatively, theaugmented indicator may be an arrow, bracket, finger or line. In someembodiments, the augmented indicator may change in appearance (e.g.transition from blinking to steady or arrow to check mark, or disappear)or color to alert the user that a step has been successfully completed.In other examples, the augmented indicator may change when the specificcable is successfully connected to the specific port

The real-time video stream may be monitored throughout the course of therepair/assembly (step 76). In this way, the system may move, resize orotherwise adjust the augmented movable indicators to reflect the user'sprogress (step 78). In one embodiment, the at least one processor may befurther configured to adjust the positions of the movable augmentedindicator by changing a pointing direction of the arrow to coincide withmovement of the specific port in the live video stream. For example,system 60 may adjust the movable augmented indicator to account for thechanging locations of the specific port in the real-time video stream.Consistent with one embodiment, after the specific cable is connected tothe specific port, the video stream may be analyzed to confirm that thespecific cable was properly connected to the specific port. The videostream may be analyzed, for example, by assessing a status light on theelectronic appliance.

In some embodiments, the method further comprises retrieving frominformation about the electronic appliance from a database. Where, forexample, the plurality of ports in the electronic appliance share asimilar visual appearance, a specific port of the plurality of ports maybe identified based on the retrieved information. Specifically, thedatabase records may be stored on a remote server or on the user'smobile device. This may be useful when mobile device connectivity ispoor or non-existent, or connectivity is unnecessary. If the user'sdevice has temporary connectivity over a communication network (e.g., tothe cloud), the best working solution can be downloaded to the user'sdevice using the same user selection, and/or automatic identificationprocedures, either manually or by pattern recognition techniques.Methods for visually augmenting the real-time video stream may be storedon various non-transitory computer readable media (CRM). Theinstructions contained on the CRM may be executed by a processor tocause the processor to perform the method discussed above.

In accordance with some embodiments, the method may further includecausing auditory feedback to be provided in conjunction with themoveable augmented indicator to guide the user's connection of thespecific cable to the specific port. Optionally, the feedback may beadjusted to account for the changing locations of the specific port inthe real-time video stream. The auditory feedback may include discrete“beeps” or “clicks”, wherein a frequency of auditory feedback correlatesto the user's proximity to the specific port. Additionally, oralternatively, the auditory feedback may include a chime or otherdistinct sound when the specific cable is properly placed in thespecific port.

Although reference is made to an electrical appliance and cable, it isto be understood that the system and methods disclosed herein may beused with other objects. Where two objects are concerned, for example,the two objects include may two distinct components of an electricalappliance (e.g. cable and port) or a user's hand and a component of theelectrical appliance (e.g. button, switch, cable, port, trigger,indicator light etc.) The augmented indicator may direct the user to aparticular component of an electrical appliance (e.g. button, switch,cable, port, trigger etc.) to guide manipulation or use thereof. The twoobjects may also include two distinct components of furniture (e.g. railand drawer), a user's hand and a component of the furniture (e.g. leg,shelf, rail, drawer etc.), or a user's hand and a tool (e.g. screwdriver, wrench, bolt etc.). The augmented indicator may direct the userto a particular component of the furniture (e.g. leg, shelf, rail,drawer etc.) to guide manipulation or use thereof.

In accordance with some embodiments, the video stream is captured by awearable image sensor or mobile device, e.g. smart glasses, mobilephone, tablet, laptop, etc.

In accordance with some embodiments, information about the electronicdevice (e.g. port and cable orientation and configuration) may be storedin a cloud or other database.

In accordance with some embodiments, the moveable augmented indicatorincludes an arrow, finger, line or boundary (e.g. a portion of a circle,square or other polygon). The indicator may change in appearance (e.g.transition from blinking to steady or arrow to check mark) or color toalert the user that a step has been successfully completed.

In accordance with some embodiments, auditory feedback is provided inconjunction with the moveable augmented indicator to guide the user to adesired component of the electrical appliance,

In accordance with some embodiments the auditory feedback may includediscrete “beeps” or “clicks”, wherein a frequency of auditory feedbackcorrelates to the user's proximity to the desired component.Additionally, or alternatively, the auditory feedback may include achime or other distinct sound when a component is properly placed and/orwhen an entire task is completed. Alternatively, the auditory feedbackmay include words.

In some embodiments, processing system 60 is configured to suspendfurther instruction when the electrical appliance or functional elementare not within the image sensor's field of view. The processor may beconfigured to notify the user, via the mobile device, when instructionshave been suspended and/or when they have resumed. The processer mayfurther be configured to notify the user, via the mobile device, of oneor more pending instructions once instructions have been suspended.

FIGS. 8A-8D illustrate another application of image processing system60, wherein the image processing system 60 employs artificialintelligence during technical support. The image processing systemcomprises at least one processor configured to: receive image datacaptured by an image sensor of a mobile device, the image data includingimages of an inoperative appliance in an environment of a user; performimage recognition on the image data to determine an identity of theinoperative appliance and a likely cause of inoperability; retrieve aplurality of sequential instructions to be provided for enabling a userto complete a plurality of sequential actions in order to remedy theinoperability; cause the mobile device to sequentially display theplurality of sequential instructions; detect that the inoperativeappliance is outside a field of view of the image sensor, based on theimage data and during execution of the sequential actions; suspenddisplay of additional sequential instructions while the inoperativeappliance is outside of the field of view; detect when the inoperativeappliance returns to the field of view after suspending display; andresume display of the additional sequential instructions after theinoperative appliance is detected to return to the field of view.

As seen in FIG. 8A, once an appliance 83 e and relevant functionalelements 83 f, 83 h, and 83 i are detected and identified in the livevideo stream, and a likely source of inoperability is assessed, aplurality of sequential instructions 85 for repairing/assembling theobject are displayed on the mobile device. A likely source of error maybe derived from database 36 r as discussed above, or deduced from theoperational state of the elements. For example, if the processordetects, using the data processing techniques described above, that theappliance is not connected to a power source, it may deduce that a lackof power is the cause of inoperability. Instructions 85 may be displayedone at a time, or multiple instructions may be displayed simultaneously.FIG. 8A illustrates yet another embodiment wherein at least twoinstructions and a moveable augmented indicator 84 a are simultaneouslydisplayed on a mobile device 81.

During repair/assembly, the user may purposefully or accidently move theappliance/functional element out of the field of view (see FIG. 8B).Optionally, the image processing system 60 is configured to detectwhether the inoperative appliance is outside a field of view of theimage sensor by analyzing the image data in which the sequential actionsare being conducted and determining that the appliance is missing fromthe image data. The processor may detect whether the inoperativeappliance is outside the field of view of the image sensor by comparingcurrent image data with data reflective of the inoperative appliance anddetermining that the inoperative appliance is not present in the currentimage data.

When the processor detects the move, it may suspend further instructionas illustrated in FIG. 8B and/or notify user 33 that theobject/functional element is out of the field of view. In either case,the processor may instruct the user to refocus the image sensor so thatthe object/functional element return to the field of view. Notificationsmay be audible, coming through a speaker of the mobile device, orvisual. Visual notification may include a verbal message displayed onthe mobile device display, or a non-verbal cue, e.g. an arrow, directingthe user to move the image sensor in a particular direction to recapturethe object/functional element in the field of view. Alternatively, animage of pending instructions, and/or and image of the object/functionalelement prior to leaving the field of view, may be displayed on themobile device. In some embodiments, the processor is configured to causethe mobile device to present information about at least one of thesequential instructions when the processor detects that the inoperativeappliance is outside the field of view. The information may include, forexample, details pertaining to pending instructions, an image of theinoperative appliance, and corresponding annotations before it left thefield of view.

Once the object/functional element returns to the field of view, theprocessor resumes real-time instruction. Additional instructions maydisplay as the user progresses. As illustrated in FIG. 8C, aconfirmation notification may accompany the completed step. Confirmationmay be audible or visual as described above. FIG. 8D illustrates amobile device display after two steps have been completed. It isenvisioned that confirmation notifications will accompany each completedstep and that a final confirmation may be provided to signify thatrepair/assembly is complete. Although steps may be displayedsequentially, the user may override or speed up an instruction toadvance to a later stage in the assembly/repair process. This optionallows skilled users to save time.

FIG. 9 is a flow chart illustrating a method for employing A.I. duringtechnical support.

Mobile device 81 transmits and image processing system 60 receives imagedata including images of inoperative appliance 83 e and variousfunctional elements related thereto (step 90). The processor mayidentify the appliance using the appliance model number or brand(illustrated as “XXX” in FIGS. 8A-8D). The image processing system 60then performs image recognition on the image data to identify theappliance's likely source of inoperability (step 92). As discussed abovewith respect to FIG. 3, a processing utility 12 p including imagerecognition, OCR, and image processing modules, is used to process theimage data received from the user's mobile device, and identifyissues/defects related to the captured object. Once the source ofinoperability is identified, the processor retrieves at least onesolution from database 36 r (step 94) and causes the mobile device todisplay one or more sequential instructions relating to the solution(step 96). In some embodiments, displaying the plurality of sequentialinstructions for remedying the inoperability of the appliance includesdisplaying a movable augmented indicator together with the image data.The movable augmented indicator may be anchored to a specific functionalelement of the inoperative appliance using tracker 36 t discussed above.In still other embodiments, audible instructions may accompany displayof the sequential instructions and/or moveable augmented indicators,

During repair/assembly of the inoperative appliance, the processorcontinuously scans the image data to determine whether theappliance/related functional elements are within the image sensor'sfield of view (FOV) (step 98). The processor may determine that theappliance/functional element is no longer within the field of view bycomparing a previous image of the appliance/functional element to a liveimage of the appliance/functional element.

If the processor determines that the appliance/functional element hasleft the field of view, the processor may suspend display of any pendinginstructions (including moveable augmented indicators) and/or instructthe user to refocus the image sensor so that the appliance/functionalelements return to the field of view (step 99). The processor may notifythe user via a visual or audible message. Such notifications may occurautomatically or after a preset time.

For example, the processor may allot three minutes to a particular stepand only notify the user that the appliance/functional element is out ofthe field of view once three minutes have passed. Such a configurationallows the user to set the mobile device aside and proceed withrepair/assembly without receiving distracting notifications. In someembodiments, the processor will only notify the user if a functionalelement related to a particular step is outside the field of view. Forexample, if step three of a set of sequential instructions directs theuser to insert cable “b” into port “z”, and cable “c” or ports v-y areoutside the field of view, the user will not receive a notification. If,however, step four requires the user to insert cable “c” into port “y”,a notification will occur. The user may continue to get notificationsuntil the relevant functional elements/appliance are within the field ofview. Alternatively, the processor may continue to provide audibleinstructions even though the appliance/functional element is outside thefield of view.

Once the relevant functional elements/appliance return to the field ofview, the processor will resume display of the pending instructions(step 100). Methods for employing A.I. during a technical supportsession may be stored on various CRM. The instructions contained on theCRM may be executed by a processor to cause the processor to perform themethod discussed above.

To ensure successful repair/assembly and customer satisfaction, imageprocessor 60 may be further configured to notify the user when anassembly/repair error occurs. The processor may be configured toidentify the product and the likely source of inoperability of theproduct using the product's model number, brand, or other labeling.

FIGS. 10A-10D illustrate a support session wherein the image processingsystem 60 is configured to provide automatic feedback. The imageprocessing system employs artificial intelligence to assist a userduring technical support. The image processing system comprises at leastone processor configured to: receive real-time image data captured by animage sensor of a mobile device at a location of an inoperative product,the real-time image data including at least one image of the inoperativeproduct; perform image recognition on the real-time image data toidentify a likely source of inoperability of the product; cause themobile device to display a plurality of sequential instructions formitigating inoperability of the product; determine that an error wasmade while performing the particular instruction, based on additionalreal-time image data captured following the display of the particularone of the plurality of sequential instructions; cause the mobile deviceto display an error notification when the particular instruction is notcomplied with, the error notification being displayed before asubsequent instruction is displayed; determine that the particularinstruction was subsequently complied with based on real-time image datacaptured following the notification; and cause the mobile device todisplay the subsequent instruction of the plurality of sequentialinstructions after the particular instruction is determined to have beencomplied with.

FIG. 10A illustrates an inoperative product 103 e and relevantfunctional elements (e.g. ports and cables) 103 f, 103 g, and 103 h.Once the appliance and elements are detected and identified in the livevideo stream, a likely source of error is assessed, and a set ofsequential instructions are superimposed onto the live video stream. Theproduct may be identified using the products model number, brand orother labeling (depicted herein as XXX). Instructions for mitigating theinoperability may be retrieved from database 36 r, using the identifiedlikely source of inoperability. The instructions may be in the form ofmoveable augmented indicators 104 a, 104 b as illustrated in FIGS. 10Aand 10D and displayed on mobile device 101. Alternatively, theinstructions may include a combination of moveable augmented indicatorsand written instructions as discussed above with respect to FIGS. 8A-Eand 9. The plurality of sequential instructions may relate to assemblingand/or repairing the inoperative product.

The augmented live feed illustrated in FIG. 10A instructs the user toinsert a specific cable 103 g into a specific port. The processor may beconfigured to determine, based on the real-time image data, that a useris about to make an error that is not in compliance with the particularinstruction, and to cause the mobile device to warn the user ofimpending non-compliance. Accordingly, when the user attempts to insertthe wrong cable into the specified port, an error notification 106 ispromptly displayed. This error notification may be symbolic, as seen inFIG. 10B, or include a verbal message and/or audible message (e.g.,buzzing). In some embodiments, the processor is further configured tocause the mobile device to present positive feedback after theparticular instruction is determined to have been complied with. The “X”illustrated in FIG. 10B, for example, may be replaced by the checkillustrated in FIG. 10C when the correct cable 103 g is inserted intothe specified port. Audible feedback, e.g., a chime may accompany thevisual message. Once the error is corrected, a subsequent instruction(e.g., moveable augmented indicator 104 b) may be displayed on themobile device, as shown in FIG. 10D.

In certain situations (e.g., complex assembly), the processor mayascertain whether the user has all of the necessary functional elementsin his possession. FIGS. 11A-11E illustrate an application of thedisclosed image processing system wherein the processor prompts the userto scan the inoperative product and related functional elements usingmobile device 113 e at various points during assembly/repair to confirmproper setup.

To begin, the processor identifies an appliance 111 using the productname or other labeling (illustrated as “XXX” in FIG. 11A). The processorthen identifies various functional components of the appliance via thelive video stream (FIGS. 11B-11C). The elements may be identified usingthe product's name, as shown in FIG. 11B or by alpha-numeric character(e.g. 1, 2, 3 . . . A, B, C . . . ). Next, as illustrated in FIG. 11D,the processor may superimpose a moveable augmented indicator onto thelive video stream, instructing the user to connect one or morefunctional elements of the appliance. After the instruction is received,the user may confirm that he is connecting the correct functionalelement by placing the element within the image sensor field of view.Alternatively, the processor may prompt the user to bring the functionalelement within the field of view to ensure that the instruction is beingproperly followed. This is especially useful for saving time, as theprocessor is able to notify the user of a pending mistake before itoccurs. In the embodiment illustrated herein, the user is instructed toinsert an ethernet cable into a specific port of the appliance (FIG.11D). The user is then prompted to scan the ethernet cable. If theincorrect cable is scanned (FIG. 11E), the user is notified, and theerror is prevented. As discussed above, the notification may be a visualand/or audible message. Alternatively, the error notification may changecolor (e.g., from red to green) when the user scans the correct cable(see FIG. 11F).

Once assembly/repair is complete a data record relating to the supportsession is generated and saved in the database as discussed above withrespect to FIGS. 4 and 5. The data record may include the number oferror messages issued in a given support session. By keeping track ofthe user's error rate, the processor may determine the user's technicalskill level so that future support sessions reflect the user's skill.For example, if the user commits numerous errors during theassembly/repair process, the processor may implement additional checkscans or instructions to minimize future error. Conversely, if the userskips or speeds through one or more steps, the processor may reduce thenumber of overall steps in a subsequent session.

FIG. 12 is a flow chart illustrating a method for guiding a technicalsupport session using the system disclosed above.

Mobile device 101 transmits and image processing system 60 receivesreal-time image data including images of an inoperative appliance 103 eand various functional elements related thereto (step 120). The imageprocessing system then performs image recognition on the image data toidentify the appliance's likely source of inoperability (step 122). Asdiscussed above with respect to FIG. 3, a processing utility 12 pincluding image recognition, OCR, and image processing modules, is usedto process the image data received from the user's mobile device andidentify issues/defects related to the captured object. Once the sourceof inoperability is identified, the processor retrieves at least onesolution from database 36 r and causes the mobile device to display oneor more sequential instructions relating to the solution (step 124). Theinoperative product may include a plurality of functional elements, inwhich case, the instructions may include various steps for manipulatingthe functional elements. Instructions may be visual or audible asdiscussed above. As discussed above, the instructions may includemoveable augmented indicators, written instructions or combinationsthereof. Processor 60 may use tracker 36 t to anchor the moveableaugmented indicator to the product and/or functional elements thereof.

For each step of the repair/assembly of the inoperative appliance, theprocessor continuously scans the image data to determine whether and whythe user committed an error (step 126). If the processor determines thatan error has occurred, e.g., the user connected the wrong cable to thewrong port, or connected the cable up-side down, an error notificationis displayed on the real-time image data (step 127). The errornotification may include steps for correcting the error. For example, ifthe user attempts to insert a cable into a port upside-down, the errornotification may instruct the user to correct the orientation of thecable.

In some embodiments, the method further includes assessing the user'stechnical skill based on the user's error rate and revising theinstructions to accommodate the user's skill level when the error rateexceed a predetermined threshold. If the user makes numerous errors, forexample, or the repair/assembly process is complex/requires numeroussteps, the processor may instruct the user to scan a functional elementprior to connecting it to the appliance to prevent errors fromoccurring. If the processor then determines that the error was correctedand that a step has been completed successfully, subsequent instructionswill appear (step 128). This error checking cycle may occur at each stepor after a predetermined number of steps. In some embodiments, theprocessor determines whether the particular instruction was subsequentlycomplied with by determining an operational or positional state of afunctional element of the inoperative product. For example, theprocessor may determine that instructions were complied with because afunctional element, e.g., a switch, is in the “On” position, or a seriesof buttons on an electrical appliance have illuminated.

Methods for guiding the technical support session may be stored onvarious CRM. The instructions contained on the CRM may be executed by aprocessor to cause the processor to perform the method discussed above.

While live video streams are beneficial to enable real-time feedbackduring a support session, in certain situations, the system may rely ondiscrete images. FIGS. 13A-13D illustrate a support session wherein theimage processing system instructs the user using discrete annotatedimages. The image processing system employs artificial intelligence toassist a user during technical support. The image processing system,comprises: at least one processor configured to: receive at least onefirst image of an inoperative product captured by a mobile device of auser; perform image analysis on the at least one first image to identifyin the at least one first image a status of a functional elementassociated with the inoperative product; access memory to determine areason why the product is inoperative based on the determined status ofthe functional element; cause visual guidance to be displayed by themobile device, wherein the visual guidance is associated with aplurality of sequential actions for causing the inoperative product tobecome operative; receive at least one second image of the product, thesecond image being indicative of a completion of the plurality ofsequential actions; perform image analysis on the at least one secondimage to determine that the completion of the plurality of sequentialactions caused the inoperative product to become operative; and providea notification to the user indicating problem resolution.

FIG. 13a illustrates configuration 133 e comprising two electricalappliances and functional elements therebetween. An image of theconfiguration is captured and transmitted using a mobile device 131 andreceived by image processing system 60. The processor identifies theappliances and elements within the image and determines a source ofinoperability using the data processing techniques described above. Insome embodiments, the processor is configured to perform image analysison the product to determine the product's model number, wherein thedetermined model number is used to select the visual guidance fordisplay. The processor may retrieve instructions for rendering theinoperative appliance operative from database 36 r, using the modelnumber of the product.

Once retrieved, the processor annotates the image with one or moreinstructions for correcting the source of inoperability. FIG. 13Billustrates an annotated image wherein functional elements 103 k, 1031have been identified as being in the wrong port. Annotation 134therefore directs the user to switch the position of elements 103 k and103 l to correct the configuration. Once corrected, the user may submitanother image of the configuration to the processor, and the processormay notify the user that the step was completed successfully usingpositive feedback, e.g. check mark 135 (FIG. 13C).

In some embodiments, the processor is further configured to check theinoperative product's operational status using a medium other than imageanalysis before providing the notice. For example, the processor maycheck for the presence or absence of Wi-fi to determine whether a routerwas properly repaired or installed.

Before generating a new data record 51 in database 36 r, the processormay prompt the user to test the functionality of the configuration. Forexample, the processor may cause the mobile device to visually oraudibly instruct the user to “access the internet” to determine whetherthe instructions caused an inoperative router to become operative, or toturn on the TV to determine whether the instructions caused aninoperative TV to become operative. As illustrated in FIG. 13D, the usermay capture an image of the operability. Once the image is received bythe processor, it confirms that the solution and setup were successful,and the data record is recorded.

If the instructions are unsuccessful the user may be presented with anoption to try again, or to receive human technical support. In thelatter case, the processor may be configured to initiate a supportsession with a human operator when the processor determines that thecompletion of the plurality of sequential actions failed, or requestthat a technician be dispatched.

FIG. 14 is a flow chart illustrating a method for assisting a userduring technical support using the system described above.

To begin, a mobile device transmits and image processing system 60receives at least one first image including an inoperative object andvarious functional elements relevant thereto (step 140). As discussedabove, the functional elements may include one of a button, a switch, atrigger, a port, a cable, and an indicator light.

The image processing system then performs image recognition on the imageto identify the appliance's likely source of inoperability (step 142).As discussed above with respect to FIG. 3, processing utility 12 p,including image recognition, OCR, and image processing modules, is usedto process the image data received from the user's mobile device,identify issues/defects related to the captured object (step 144). Theidentified issue may relate to the operational or positional state ofany of the functional elements. For example, an unlit indicator lightmay indicate an issue with the power source, while a switch in adownward position may indicate that an appliance has not been tuned on.

Once the source of inoperability is identified, the processor retrievesat least one solution from database 36 r related to the assembly,repair, or correction of the inoperative object and causes the mobiledevice to display one or more annotations relating to the solution (step146). In some embodiments, the annotations may be anchored to at leastone functional element of the inoperative product, such that as a cameraangle of the mobile device changes, the annotation remains anchored tothe functional element.

After one or more steps relating to the solution are complete, a secondimage of the object and relevant functional elements are transmitted tothe processor. Next, system 50 performs additional image analysis on thesecond image to determine whether the one or more steps were completed(step 148). The processor may also confirm that the object in the secondimage is a same object that was captured in the at least one firstimage.

Once it is confirmed that the solution was properly carried out, theprocessor may notify the user using positive feedback (step 150). Thenotification may be visually provided to the user's mobile device oraudibly provided to the user via a speaker of the mobile device.Notifications may be provided after each step is completed and/or afterall steps have been completed.

It is to be understood that the processor may provide negative feedbackin the form of the error notification discussed above, and providefurther instruction to facilitate assembly/repair. In some embodiments,a different set of sequential actions may be displayed when theprocessor determines, based on additional images, that the objectremains inoperative. Consistent with other disclosed embodiments,methods for guiding the technical support session may be stored onvarious CRM. The instructions contained on the CRM may be executed by aprocessor to cause the processor to perform the method discussed above.

Although the system and method described herein relate to an electricalappliance, it is to be understood that the same principles apply to anyof a variety of equipment or objects including, e.g., furniture. Thesystem may, for example, identify a plurality of rails, a head board anda foot board, and instruct the user on proper assembly using one or moremoveable augmented indicators.

It should also be understood that in the processes or methods describedabove, the steps of the processes/methods may be performed in any orderor simultaneously, unless it is clear from the context that one stepdepends on another being performed first.

The technology disclosed herein can be implemented by software that canbe integrated into existing CRM systems of technical support centers ororganizations, and that can replace it or work in parallel thereto. Suchsoftware implementations combine opening a voiceless bi-directionalvideo channel, when the customer's smartphone transmits the video imageto the agent/expert, and the agent/expert gives the customer audiovisualinstructions over the communication channel.

Accordingly, the systems and methods disclosed herein enable users toproactively diagnose faulty item/equipment for increasing theproductivity and efficiency, and to resolve issues faster based on amaintained pool of past working solutions. The user's mobile device,e.g., 31 is thereby harnessed to conduct support sessions and improvecustomer satisfaction, decrease technician dispatch rates for resolvinguser's problems, substantially improve the first call resolution rates,and decrease the average handling time.

As will be appreciated by one skilled in the art, aspects of the presentdisclosure may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present disclosure may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

As described hereinabove and shown in the associated figures, thepresent disclosure provides support session techniques, systems andmethods, for expeditiously identifying product defects/other issues andcorresponding working solutions for resolving problems encountered byremote users. While particular embodiments of the disclosure have beendescribed, it will be understood, however, that the disclosure is notlimited thereto, since modifications may be made by those skilled in theart, particularly in light of the foregoing teachings. As will beappreciated by the skilled person, the disclosure can be carried out ina great variety of ways, employing more than one technique from thosedescribed above, all without exceeding the scope of the claims.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in a database, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, or other opticaldrive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

1. An image processing system employing artificial intelligence duringtechnical support, the image processing system comprising: at least oneprocessor configured to: receive image data captured by an image sensorof a mobile device, the image data including images of an inoperativeappliance in an environment of a user; perform image recognition on theimage data to identify the inoperative appliance and determine a likelycause of inoperability; retrieve a plurality of sequential instructionsto be provided for enabling a user to complete a plurality of sequentialactions in order to remedy the inoperability; cause the mobile device tosequentially display the plurality of sequential instructions; detectthat the inoperative appliance is outside a field of view of the imagesensor, based on the image data and during execution of the sequentialactions; suspend display of additional sequential instructions when theinoperative appliance is outside of the field of view; detect when theinoperative appliance returns to the field of view after suspendingdisplay; and resume display of the additional sequential instructionsafter the inoperative appliance is detected to return to the field ofview.
 2. The image processing system of claim 1, wherein the at leastone processor is configured to detect that the inoperative appliance isoutside a field of view of the image sensor by analyzing the image dataduring conduct of the sequential actions and determining that theappliance is missing from the image data.
 3. The image processing systemof claim 1, wherein the at least one processor is configured to detectthat the inoperative appliance is outside the field of view of the imagesensor by comparing current image data with data reflective of theinoperative appliance and determining that the inoperative appliance isnot present in the current image data.
 4. The image processing system ofclaim 1, wherein the mobile device includes at least one of a mobilephone, a tablet, a laptop, and a wearable sensor.
 5. The imageprocessing system of claim 4, wherein the wearable sensor is integratedinto glasses.
 6. The image processing system of claim 1, wherein the atleast one processor is configured to, upon suspending display, notifythe user, through the mobile device, that the subsequent instructionshave been suspended.
 7. The image processing system of claim 1, whereinwhen the at least one processor detects that the inoperative applianceis outside the field of view of the image sensor, the at least oneprocessor is configured to cause the mobile device to presentinformation about at least one of the sequential instructions.
 8. Theimage processing system of claim 1, wherein the at least one processoris further configured to: identify when a part of the inoperativeappliance is outside the field of view of the image sensor, wherein thepart of the inoperative appliance includes at least one functionalelement; suspend display of the additional sequential instructions whilethe part of the inoperative appliance is outside of the field of view;present to the user a request to capture the inoperative appliance froma different angle, such that the part of the inoperative appliancereturns to the field of view; and resume displaying of sequentialinstructions after the part of the inoperative appliance returns to thefield of view.
 9. The image processing system of claim 1, wherein the atleast one processor is further configured to access information storedin a database to identify the inoperative appliance.
 10. The imageprocessing system of claim 1, wherein the at least one processor isfurther configured to access information stored in a database todetermine the likely cause of inoperability.
 11. The image processingsystem of claim 1, wherein the at least one processor is furtherconfigured to determine the likely cause of inoperability by identifyingin the image data an operational status of at least one functionalelement of the inoperative appliance.
 12. A non-transitory computerreadable medium including instructions for employing artificialintelligence during technical support, the instructions being executableby at least one processor to cause the at least one processor to performa method, the method comprising: receiving image data captured by animage sensor of a mobile device, the image data including images of aninoperative appliance in an environment of a user; performing imagerecognition on the image data to identify the inoperative appliance anddetermine a likely cause of inoperability; retrieving a plurality ofsequential instructions to be provided for enabling a user to complete aplurality of sequential actions in order to remedy the inoperability;causing the mobile device to sequentially display the plurality ofsequential instructions; detecting that the inoperative appliance isoutside a field of view of the image sensor based on the image data andduring execution of the sequential actions; suspending display ofadditional sequential instructions while the inoperative appliance isoutside of the field of view; detecting when the inoperative appliancereturns to the field of view after suspended display; and resumingdisplay of the additional sequential instructions after detecting thatthe inoperative appliance has returned to the field of view.
 13. Thecomputer readable medium of claim 12, wherein the image data includes avideo stream.
 14. The computer readable medium of claim 12, wherein theimage data includes a plurality of discrete still images.
 15. Thecomputer readable medium of claim 12, wherein the inoperative applianceis identified using at least one of a model number of the inoperativeappliance, a product number of the inoperative appliance, or a brand ofthe inoperative appliance.
 16. The computer readable medium of claim 12,wherein displaying the plurality of sequential instructions forremedying the inoperability of the appliance includes displaying amovable augmented indicator together with the image data, wherein themovable augmented indicator is anchored to a specific functional elementof the inoperative appliance.
 17. The computer readable medium of claim16, wherein suspending the display of the additional sequentialinstructions while the inoperative appliance is outside of the field ofview includes suspending a display of the movable augmented indicator,and wherein resuming a display of the sequential instructions after theinoperative appliance returns to the field of view includes resuming adisplay of the movable augmented indicator.
 18. The computer readablemedium of claim 12, wherein at least some of the additional sequentialinstructions are given audibly.
 19. The computer readable medium ofclaim 18, wherein the audible instructions continue while theinoperative appliance is outside of the field of view.
 20. A method foremploying artificial intelligence during technical support, the methodcomprising: receiving image data captured by an image sensor of a mobiledevice, the image data including images of an inoperative appliance inan environment of a user; performing image recognition on the image datato identify the inoperative appliance and determine a likely cause ofinoperability; retrieving a plurality of sequential instructions to beprovided for enabling a user to complete a plurality of sequentialactions in order to remedy the inoperability; causing the mobile deviceto sequentially display the plurality of sequential instructions;detecting that the inoperative appliance is outside a field of view ofthe image sensor based on the image data and during execution of thesequential actions; suspending display of additional sequentialinstructions while the inoperative appliance is outside of the field ofview; detecting when the inoperative appliance returns to the field ofview after suspended display; and resuming display of the additionalsequential instructions after detecting that the inoperative appliancehas returned to the field of view.